Methods, apparatus, application specific integrated circuits (ASIC)s and other embodiments associated with analyzing a cancerous prostate using group-sparse non-negative canonical correlation analysis (GNCCA) with a variable importance in the projections (VIP) score are described. One example apparatus includes a set of logics that acquires a set of features from a plurality of feature views of a region of tissue demonstrating cancerous pathology, produces a ranked set of discriminative features using GNCCA with the VIP score, optimizes computation of the GNCCA using a vector-block coordinate descent (BCD) approach, and provides a prostate cancer (CaP) grade or a biochemical recurrence (BcR) score based on the set of discriminative features. Embodiments of example apparatus may generate and display the CaP grade, BcR score, or set of discriminative features.
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